A one-line status update and a complex quote review are not the same job, and paying premium model prices for both is one of the easiest ways to overspend on AI. The fix is not a bigger budget. It is routing.
What routing actually does
A router sits between the request and the model providers, and sends each task to the model that fits it: cost, latency, and quality all weighed per request, not decided once for the whole system. A simple classification or lookup goes to a cheap, fast model. A task that needs real reasoning goes to a premium one. In the RouteLLM research from UC Berkeley, a learned router cut cost by over 85% on one benchmark (MT Bench) while keeping 95% of GPT-4's quality. On harder benchmarks the saving was smaller, 35% to 45%, which is the honest range to plan around.
Why the trend is toward cheaper, not more expensive
Model prices fall as new, cheaper models enter the market and as competition between providers intensifies. A business locked into one model does not benefit when a cheaper option appears elsewhere. A business built on routing captures that saving automatically, the moment a better option exists, without anyone having to re-architect anything.
What this looks like in practice
The arithmetic is simple. If a lightweight model costs a tenth of a premium one per request, and 70% of your requests can go to it, total model spend falls to 37% of what it was: a cut of almost two thirds. Your own ratio will differ. The point holds because most requests in a real business are not hard reasoning problems. They are lookups, summaries and drafts, and a lightweight model handles those well.